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knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(warning = FALSE)
ma_data <- read_csv(DATA_PATH)
Parsed with column specification:
cols(
.default = col_character(),
x1 = [32mcol_double()[39m,
mean_age = [32mcol_double()[39m,
productive_vocab_mean = [32mcol_double()[39m,
productive_vocab_median = [32mcol_double()[39m,
n_train_test_pair = [32mcol_double()[39m,
n_test_trial_per_pair = [32mcol_double()[39m,
n_repetitions_sentence = [32mcol_double()[39m,
n_repetitions_video = [32mcol_double()[39m,
inclusion_certainty = [32mcol_double()[39m,
n_1 = [32mcol_double()[39m,
x_1 = [32mcol_double()[39m,
x_2 = [32mcol_double()[39m,
x_2_raw = [32mcol_double()[39m,
sd_1 = [32mcol_double()[39m,
sd_2 = [32mcol_double()[39m,
sd_2_raw = [32mcol_double()[39m,
t = [32mcol_double()[39m,
d = [32mcol_double()[39m,
d_calc = [32mcol_double()[39m,
d_var_calc = [32mcol_double()[39m
)
See spec(...) for full column specifications.
ma_data
ALL_CATEGORICAL_VARS <- c("test_type","presentation_type",
"agent_argument_type_clean", "patient_argument_type_clean",
"stimuli_modality", "stimuli_actor", "character_identification", "practice_phase", "test_mass_or_distributed", "test_method")
get_cross_counts <- function(args, df){
var1 = args[[1]]
var2 = args[[2]]
if (var1 != var2){
df %>%
select_(var1, var2) %>%
rename(v1 = var1,
v2 = var2) %>%
count(v1, v2) %>%
mutate(v1_long = glue("{var1}/{v1}"),
v2_long = glue("{var2}/{v2}")) %>%
select(v1_long, v2_long, n)
}
}
all_pair_counts <- list(ALL_CATEGORICAL_VARS,
ALL_CATEGORICAL_VARS) %>%
cross() %>%
map_df(get_cross_counts, ma_data) %>%
complete(v1_long, v2_long, fill = list(n = 0)) %>%
filter(v1_long != v2_long)
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preserve their attributes
all_counts_wide <- all_pair_counts %>%
pivot_wider(names_from = v2_long, values_from = n)
all_counts_wide_matrix <- all_counts_wide %>%
select(-v1_long) %>%
as.matrix()
row.names(all_counts_wide_matrix) <- all_counts_wide$v1_long
heatmaply(all_counts_wide_matrix,
fontsize_row = 8,
fontsize_col = 8)
http://www.metafor-project.org/doku.php/tips:model_selection_with_glmulti_and_mumin
res_reasonable <- glmulti(d_calc ~mean_age + sentence_structure + agent_argument_type_clean + patient_argument_type_clean + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + test_method, data=ma_data, level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=32)
Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.
After 50 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed
Crit= 718.764861896471
Mean crit= 765.342982684011
After 100 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Crit= 707.535751556097
Mean crit= 741.900644563329
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.
After 150 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Crit= 707.535751556097
Mean crit= 733.065259509332
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.
After 200 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Crit= 707.535751556097
Mean crit= 726.519509135412
Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.
After 250 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Crit= 707.535751556097
Mean crit= 721.810858847122
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.
After 300 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Crit= 707.535751556097
Mean crit= 717.250000345278
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.
After 350 models:
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification
Crit= 707.535751556097
Mean crit= 716.478095989443
Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.
After 400 models:
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification+test_method
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification+test_method
Crit= 706.583408888092
Mean crit= 713.231492327172
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.
After 450 models:
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification+test_method
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification+test_method
Crit= 706.583408888092
Mean crit= 713.143895286831
Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.
After 500 models:
Best model: d_calc~1+mean_age+sentence_structure+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification+test_method
Best model: d_calc~1+mean_age+agent_argument_type_clean+patient_argument_type_clean+test_mass_or_distributed+character_identification+test_method
Crit= 706.583408888092
Mean crit= 712.038939733654
Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.Rows with NAs omitted from model fitting.Redundant predictors dropped from the model.
Completed.
top <- weightable(res_reasonable)
top <- top[top$aicc <= min(top$aicc) + 2,]
summary(res_reasonable@objects[[1]])
Multivariate Meta-Analysis Model (k = 110; method: ML)
logLik Deviance AIC BIC AICc
-339.6835 707.6115 703.3669 735.7727 706.5834
Variance Components: none
Test for Residual Heterogeneity:
QE(df = 98) = 707.6115, p-val < .0001
Test of Moderators (coefficients 2:12):
QM(df = 11) = 107.7326, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.0964 0.1580 0.6099 0.5419 -0.2133 0.4060
mean_age 0.0001 0.0001 0.5035 0.6146 -0.0002 0.0004
sentence_structuretransitive -0.2883 0.1465 -1.9685 0.0490 -0.5754 -0.0013 *
agent_argument_type_cleannoun_phrase 0.1776 0.1426 1.2453 0.2130 -0.1019 0.4572
agent_argument_type_cleanpronoun -1.0179 0.1740 -5.8508 <.0001 -1.3589 -0.6769 ***
agent_argument_type_cleanvarying_agent -0.2261 0.1248 -1.8118 0.0700 -0.4707 0.0185 .
patient_argument_type_cleannoun 0.1984 0.1416 1.4008 0.1613 -0.0792 0.4760
patient_argument_type_cleannoun_phrase 1.3116 0.1915 6.8503 <.0001 0.9363 1.6868 ***
patient_argument_type_cleanpronoun 1.1989 0.2025 5.9210 <.0001 0.8020 1.5958 ***
test_mass_or_distributedmass 0.6258 0.0994 6.2956 <.0001 0.4309 0.8206 ***
character_identificationyes 0.2496 0.0718 3.4768 0.0005 0.1089 0.3902 ***
test_methodpoint 0.2024 0.1086 1.8641 0.0623 -0.0104 0.4152 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#summary(res_reasonable@objects[[2]])
plot(res_reasonable, type="s")
summary(res_reasonable@objects[[2]])
Multivariate Meta-Analysis Model (k = 110; method: ML)
logLik Deviance AIC BIC AICc
-339.6835 707.6115 703.3669 735.7727 706.5834
Variance Components: none
Test for Residual Heterogeneity:
QE(df = 98) = 707.6115, p-val < .0001
Test of Moderators (coefficients 2:12):
QM(df = 11) = 107.7326, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.0964 0.1580 0.6099 0.5419 -0.2133 0.4060
mean_age 0.0001 0.0001 0.5035 0.6146 -0.0002 0.0004
agent_argument_type_cleannoun_phrase 0.1776 0.1426 1.2453 0.2130 -0.1019 0.4572
agent_argument_type_cleanpronoun -1.0179 0.1740 -5.8508 <.0001 -1.3589 -0.6769 ***
agent_argument_type_cleanvarying_agent -0.2261 0.1248 -1.8118 0.0700 -0.4707 0.0185 .
patient_argument_type_cleannoun -0.0899 0.1003 -0.8962 0.3701 -0.2866 0.1067
patient_argument_type_cleannoun_phrase 1.0232 0.1384 7.3922 <.0001 0.7519 1.2945 ***
patient_argument_type_cleanpronoun 0.9106 0.1379 6.6008 <.0001 0.6402 1.1809 ***
patient_argument_type_cleanvarying_patient -0.2883 0.1465 -1.9685 0.0490 -0.5754 -0.0013 *
test_mass_or_distributedmass 0.6258 0.0994 6.2956 <.0001 0.4309 0.8206 ***
character_identificationyes 0.2496 0.0718 3.4768 0.0005 0.1089 0.3902 ***
test_methodpoint 0.2024 0.1086 1.8641 0.0623 -0.0104 0.4152 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
rma.glmulti <- function(formula,data)
{rma.mv(formula, d_var_calc, data=ma_data, method="ML")
}
res_sink <- glmulti(d_calc ~mean_age + sentence_structure + agent_argument_type_clean + patient_argument_type_clean + test_type + stimuli_actor + stimuli_modality + presentation_type + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + n_repetitions_video + test_method, data=ma_data, level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=32)
#print(res_sink)
top <- weightable(res_sink)
top <- top[top$aicc <= min(top$aicc) + 2,]
top
summary(res_sink@objects[[1]])
summary(res_sink@objects[[2]])
plot(res_sink, type="s")
eval(metafor:::.glmulti)
coef(res_sink)
mmi <- as.data.frame(coef(res_sink))
mmi <- data.frame(Estimate=mmi$Est, SE=sqrt(mmi$Uncond), Importance=mmi$Importance, row.names=row.names(mmi))
mmi$z <- mmi$Estimate / mmi$SE
mmi$p <- 2*pnorm(abs(mmi$z), lower.tail=FALSE)
names(mmi) <- c("Estimate", "Std. Error", "Importance", "z value", "Pr(>|z|)")
mmi$ci.lb <- mmi[[1]] - qnorm(.975) * mmi[[2]]
mmi$ci.ub <- mmi[[1]] + qnorm(.975) * mmi[[2]]
mmi <- mmi[order(mmi$Importance, decreasing=TRUE), c(1,2,4:7,3)]
round(mmi, 4)
three interdependence moderators, take one ### take test type
rma.glmulti <- function(formula,data)
{rma.mv(formula, d_var_calc, data=ma_data, method="ML")
}
res_test_type <- glmulti(d_calc ~mean_age + sentence_structure + agent_argument_type_clean + patient_argument_type_clean + test_type + stimuli_actor + presentation_type + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + n_repetitions_video, data=ma_data, level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=32)
print(res_test_type)
top <- weightable(res_test_type)
top <- top[top$aicc <= min(top$aicc) + 2,]
top
summary(res_test_type@objects[[1]])
plot(res_test_type, type="s")
summary(res_test_type@objects[[2]])
plot(res_test_type, type="s")
eval(metafor:::.glmulti)
coef(res_test_type)
mmi <- as.data.frame(coef(res_test_type))
mmi <- data.frame(Estimate=mmi$Est, SE=sqrt(mmi$Uncond), Importance=mmi$Importance, row.names=row.names(mmi))
mmi$z <- mmi$Estimate / mmi$SE
mmi$p <- 2*pnorm(abs(mmi$z), lower.tail=FALSE)
names(mmi) <- c("Estimate", "Std. Error", "Importance", "z value", "Pr(>|z|)")
mmi$ci.lb <- mmi[[1]] - qnorm(.975) * mmi[[2]]
mmi$ci.ub <- mmi[[1]] + qnorm(.975) * mmi[[2]]
mmi <- mmi[order(mmi$Importance, decreasing=TRUE), c(1,2,4:7,3)]
round(mmi, 4)
res_test_method <- glmulti(d_calc ~mean_age + sentence_structure + agent_argument_type_clean + patient_argument_type_clean + stimuli_actor + presentation_type + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + n_repetitions_video + test_method, data=ma_data, level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=32)
print(res_test_method)
top <- weightable(res_test_method)
top <- top[top$aicc <= min(top$aicc) + 2,]
top
summary(res_test_method@objects[[1]])
plot(res_test_method, type="s")
summary(res_test_method@objects[[2]])
plot(res_test_method, type="s")
eval(metafor:::.glmulti)
coef(res_test_method)
mmi <- as.data.frame(coef(res_test_method))
mmi <- data.frame(Estimate=mmi$Est, SE=sqrt(mmi$Uncond), Importance=mmi$Importance, row.names=row.names(mmi))
mmi$z <- mmi$Estimate / mmi$SE
mmi$p <- 2*pnorm(abs(mmi$z), lower.tail=FALSE)
names(mmi) <- c("Estimate", "Std. Error", "Importance", "z value", "Pr(>|z|)")
mmi$ci.lb <- mmi[[1]] - qnorm(.975) * mmi[[2]]
mmi$ci.ub <- mmi[[1]] + qnorm(.975) * mmi[[2]]
mmi <- mmi[order(mmi$Importance, decreasing=TRUE), c(1,2,4:7,3)]
round(mmi, 4)
res_stimuli_modality <- glmulti(d_calc ~mean_age + sentence_structure + agent_argument_type_clean + patient_argument_type_clean + stimuli_actor + stimuli_modality + presentation_type + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + n_repetitions_video, data=ma_data, level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=32)
print(res_stimuli_modality)
top <- weightable(res_stimuli_modality)
top <- top[top$aicc <= min(top$aicc) + 2,]
top
summary(res_stimuli_modality@objects[[1]])
plot(res_stimuli_modality, type="s")
summary(res_stimuli_modality@objects[[2]])
plot(res_stimuli_modality, type="s")
eval(metafor:::.glmulti)
coef(res_stimuli_modality)
mmi <- as.data.frame(coef(res_stimuli_modality))
mmi <- data.frame(Estimate=mmi$Est, SE=sqrt(mmi$Uncond), Importance=mmi$Importance, row.names=row.names(mmi))
mmi$z <- mmi$Estimate / mmi$SE
mmi$p <- 2*pnorm(abs(mmi$z), lower.tail=FALSE)
names(mmi) <- c("Estimate", "Std. Error", "Importance", "z value", "Pr(>|z|)")
mmi$ci.lb <- mmi[[1]] - qnorm(.975) * mmi[[2]]
mmi$ci.ub <- mmi[[1]] + qnorm(.975) * mmi[[2]]
mmi <- mmi[order(mmi$Importance, decreasing=TRUE), c(1,2,4:7,3)]
round(mmi, 4)